Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

An efficient algorithm for generating generalized decision forests

Abstract: A shortcoming of univariate decision tree learners is that they do not learn intermediate concepts and select only one of the input features in the branching decision at each intermediate tree node. It has been empirically demonstrated that cascading other classification methods, which learn intermediate concepts, with decision tree learners can alleviate such representational bias of decision trees and potentially improve classification performance. However, a more complex model that fits training data better may not necessarily perform better on unseen data, commonly referred to as the overfitting problem. To find the most appropriate degree of such cascade generalization, a decision forest (i.e., a set of decision trees with other classification models cascaded to different degrees) needs to be generated, from which the best decision tree can then be identified. In this paper, the authors propose an efficient algorithm for generating such decision forests. The algorithm uses an extended decision tree data structure and constructs any node that is common to multiple decision trees only once. The authors have empirically evaluated the algorithm using 32 data sets for classification problems from the University of California, Irvine (UCI) machine learning repository and report on results demonstrating the efficiency of the algorithm in this paper.

A diagnostic tree model for polytomous responses with multiple strategies.

Abstract: Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students' problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students' partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM.

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution.

Abstract: The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft® Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.

Genetically Engineered Decision Trees: Population Diversity Produces Smarter Trees

Abstract: When considering a decision tree for the purpose of classification, accuracy is usually the sole performance measure used in the construction process. In this paper, we introduce the idea of combining a decision tree's expected value and variance in a new probabilistic measure for assessing the performance of a tree. We develop a genetic algorithm for constructing a tree using our new measure and conduct computational experiments that show the advantages of our approach. Further, we investigate the effect of introducing diversity into the population used by our genetic algorithm. We allow the genetic algorithm to simultaneously focus on two distinct probabilistic measures--one that is risk averse and one that is risk seeking. Our bivariate genetic algorithm for constructing a decision tree performs very well, scales up quite nicely to handle data sets with hundreds of thousands of points, and requires only a small percent of the data to generate a high-quality decision tree. We demonstrate the effectiveness of our algorithm on three large data sets.

Point cloud data based single tree three-dimensional modeling and morphological parameter extracting method

Abstract: The invention relates to a point cloud data based single tree three-dimensional modeling and morphological parameter extracting method. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method comprises obtaining three-dimensional surface point cloud data of high density standing trees through a three-dimensional scanner or other live-action measuring modes, calculating the shortest distance from points to root nodes through a k-nearest neighbor graph, performing hierarchical clustering on the data according to distance, enabling centers of clustering hierarchies to be served as framework points of a limb system and meanwhile extracting corresponding semi-diameter of the framework points; connecting the framework points to establish a topological structure of branches and grading the branches; performing three-dimensional geometrical reconstruction on branches through a generalized cylinder body; adding leaf models to the limb system to form into a vivid three-dimensional single tree model; extracting height of trees, diameter of breast height and crown breadth of the standing trees in the point cloud. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method can rapidly and semi-automatically extract tree important geometrical parameters and topological information to form into the high vivid single tree geometric model and has wide application prospects and values in fields such as agriculture and forestry survey, ecological research and landscape planning.